Description

You will design and deploy predictive models to enable intelligent home energy decisions, including forecasting comfort, cost, and optimizing EV charging and demand response.

Responsibilities

• Build and deploy advanced models for occupancy, runtime, cost forecasting, anomaly detection, and preconditioning.
• Optimize energy operations including Demand Response (DR), Time-of-Use (TOU) shifting, and Virtual Power Plant (VPP) strategies.
• Partner with data engineering to transform legacy data structures into documented, reusable data products for ML and real-time analytics.
• Collaborate with product, engineering, and analytics teams to embed intelligence into production systems.
• Translate complex model outcomes into actionable insights for technical and non-technical audiences.

Required Skills

• 5+ years of energy industry experience with technical leadership on high-impact modeling initiatives.
• Expertise in predictive modeling, forecasting, and applied ML including regression, gradient boosting, time-series, and causal inference.
• Experience with energy forecasting, thermal modeling, or Demand Response optimization.
• Knowledge of energy markets, Distributed Energy Resources (DER), and Virtual Power Plant (VPP) concepts.
• Proficiency in Python (Pandas, NumPy, scikit-learn, PySpark).
• Experience with distributed compute environments including Spark, Databricks, and GCP.
• Experience working with large-scale event and sensor data from energy, IoT, or device-driven ecosystems.
• Ability to move models from concept to production in collaboration with engineering.
• Skills in statistical analysis, feature engineering, and experimental design such as A/B testing.

Preferred Skills

• Advanced degree (MS/PhD) in Statistics, Computer Science, or Engineering.
• Familiarity with LLM or generative AI applications in analytics and optimization.

Key Skills

Education

Any Graduate